{"title":"INVERSE MODEL USING LAND AND PROPERTY SUB-SYSTEMS FOR PLANNING FUTURE CITIES: A GENERAL FRAMEWORK","authors":"R. Crespo, A. Rajabifard","doi":"10.37043/jura.2022.14.1.1","DOIUrl":null,"url":null,"abstract":"This paper suggests, based on literature review, the use of the inverse model coupled with land and property systems to support urban decision-making. The inverse model is to be used for planning decisions today to achieve the desired tomorrow. This approach has been used previously in urban planning with a property system. The use of a property system alone is insufficient in dealing with the complexity of urban systems. Complex systems are made up of sub-systems that interact with each other; the integration of two sub-systems offers a first and simple alternative to address the complexity of urban systems. We suggest the use of two parametric approaches, logistic regression and house price, to model land and property sub-systems, respectively. Finally, we stress that further studies are needed to integrate the inverse model with other statistical techniques that also deal with complexity, such as cellular automata (CA) or agent-based models.","PeriodicalId":54010,"journal":{"name":"Journal of Urban and Regional Analysis","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Urban and Regional Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37043/jura.2022.14.1.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 1
Abstract
This paper suggests, based on literature review, the use of the inverse model coupled with land and property systems to support urban decision-making. The inverse model is to be used for planning decisions today to achieve the desired tomorrow. This approach has been used previously in urban planning with a property system. The use of a property system alone is insufficient in dealing with the complexity of urban systems. Complex systems are made up of sub-systems that interact with each other; the integration of two sub-systems offers a first and simple alternative to address the complexity of urban systems. We suggest the use of two parametric approaches, logistic regression and house price, to model land and property sub-systems, respectively. Finally, we stress that further studies are needed to integrate the inverse model with other statistical techniques that also deal with complexity, such as cellular automata (CA) or agent-based models.